#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[StorableClass]
[Item("SymbolicRegressionPruningOperator", "An operator which prunes symbolic regression trees.")]
public class SymbolicRegressionPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
protected SymbolicRegressionPruningOperator(SymbolicRegressionPruningOperator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicRegressionPruningOperator(this, cloner);
}
[StorableConstructor]
protected SymbolicRegressionPruningOperator(bool deserializing) : base(deserializing) { }
public SymbolicRegressionPruningOperator() {
var impactValuesCalculator = new SymbolicRegressionSolutionImpactValuesCalculator();
Parameters.Add(new ValueParameter(ImpactValuesCalculatorParameterName, "The impact values calculator to be used for figuring out the node impacts.", impactValuesCalculator));
}
protected override ISymbolicDataAnalysisModel CreateModel() {
return new SymbolicRegressionModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper);
}
protected override double Evaluate(IDataAnalysisModel model) {
var regressionModel = (IRegressionModel)model;
var regressionProblemData = (IRegressionProblemData)ProblemData;
var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
var estimatedValues = regressionModel.GetEstimatedValues(ProblemData.Dataset, trainingIndices); // also bounds the values
var targetValues = ProblemData.Dataset.GetDoubleValues(regressionProblemData.TargetVariable, trainingIndices);
OnlineCalculatorError errorState;
var quality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState);
if (errorState != OnlineCalculatorError.None) return double.NaN;
return quality;
}
}
}